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Machine Learning for Hybrid Machine Translation

Sabine Hunsicker; Yu Chen; Christian Federmann
In: Proceedings of the Seventh Workshop on Statistical Machine Translation. Workshop on Statistical Machine Translation (WMT-12), Montréal, Québec, Canada, Pages 312-316, Association for Computational Linguistics, 6/2012.


We describe a substitution-based system for hybrid machine translation (MT) that has been extended with machine learning components controlling its phrase selection. The approach is based on a rule-based MT (RBMT) system which creates template translations. Based on the rule-based generation parse tree and target-to-target alignments, we identify the set of “interesting” translation candidates from one or more translation engines which could be substituted into our translation templates. The substitution process is either controlled by the output from a binary classifier trained on feature vectors from the different MT engines, or it is depending on weights for the decision factors, which have been tuned using MERT. We are able to observe improvements in terms of BLEU scores over a baseline version of the hybrid system.


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